Get the data ready for the next decision.
Data work starts before the dashboard. 0OP helps collect, clean, and organize operational records so reports and automations use information with a known source and an agreed meaning.
Explore the scope ↓0op.dev/dataWhere the work gets stuck
A customer may appear under several names, dates may use different time zones, and two reports may count the same event differently. Adding a chart will not resolve those disagreements. A useful data pipeline makes the definitions explicit, preserves the original records where appropriate, and shows which items could not be processed. The aim is a repeatable path from source to usable output.
A concrete example
Example scope: consolidate several departmental customer exports into a reviewable dataset. Uncertain duplicate candidates remain visible rather than being silently merged.
Input
Approved source exports, the identifiers each department uses, and a definition of the final reporting dataset.
Output
A traceable dataset, a data quality report, and a queue of records requiring an owner’s decision.
- Profile column formats, missing values, duplicate candidates, and conflicting identifiers.
- Apply documented transformations while retaining a connection to each source record.
- Check totals and publish the cleaned result alongside rejected records and unresolved matches.
When it fits
Useful before connecting reports or automations to inconsistent records. A clean format does not prove the original information is correct.
What you could receive
- Repeatable cleanup pipeline
- Field definitions and quality checks
- Exception report and correction procedure
What we need to start
- Representative source files
- Data ownership and matching rules
- Reporting questions and retention requirements
Capture a starting scope with the workflow templates. For the design choice, read agents vs automation.
Workflows built around Data
Cleanup and validation
Normalize agreed fields, identify duplicate candidates, and flag missing or inconsistent values. Keep uncertain matches available for review instead of merging records on an unsupported guess.
Repeatable data pipelines
Import from approved files or interfaces, apply documented transformations, and publish the result to the chosen destination. Make each run traceable to its inputs.
Operational reports
Build focused summaries around decisions the team actually makes. Show the reporting period, metric definitions, refresh time, and gaps that affect interpretation.
How the project comes together
Inspect representative records
Review real formats and known problem cases. Identify the owner of each source and agree on the questions the resulting dataset should answer.
Define and test transformations
Write cleanup rules and compare outputs with source samples. Track rejected records and check that totals still make sense after filtering or joining data.
Make refreshes repeatable
Run the process again with new inputs. Document how to handle schema changes, failed imports, and corrections without silently rewriting the reporting history.
Keep the boundaries explicit
Clean formatting does not prove that source data is accurate. Missing history cannot be reconstructed without evidence, and sensitive records need appropriate access and retention decisions. Definitions and unresolved quality issues remain visible alongside the output.
Questions about Data
Do we need a data warehouse?
Not always. A well-structured sheet or a small database may be sufficient. Volume, update frequency, access needs, and reporting complexity determine the appropriate setup.
Can you combine spreadsheet and application data?
Yes, when there is a reliable way to identify matching records. We check identifiers and join rules before treating values from different sources as the same item.
Can AI clean the records?
AI can help interpret inconsistent text, but uncertain classifications need checks. Deterministic transformations remain useful for dates, formats, required fields, and known mappings.
What should stop being manual?
Bring a recurring task, an example input, and the outcome you need. We can use that to define a practical first scope.
Discuss it with Oğuz ↗0OP · Zero Operation Development
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